Feature Importance Analysis of Non-coding DNA/RNA Sequences Based on Machine Learning Approaches-Reference-Cited by-同舟云学术

Feature Importance Analysis of Non-coding DNA/RNA Sequences Based on Machine Learning Approaches

Published:2021 Issue: Volume: Page:81-92
ISSN:0302-9743
Container-title:Advances in Bioinformatics and Computational Biology
language:
Short-container-title:

Author:

de Almeida Breno Lívio Silva,Queiroz Alvaro Pedroso,Santos Anderson Paulo Avila,Bonidia Robson Parmezan,da Rocha Ulisses Nunes,Sanches Danilo Sipoli,de Carvalho André Carlos Ponce de Leon Ferreira

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-91814-9_8

Reference35 articles.

1. Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2623–2631 (2019)

2. Amin, N., McGrath, A., Chen, Y.P.P.: Fexrna: exploratory data analysis and feature selection of non-coding rna. IEEE/ACM Trans. Comput. Biol. Bioinform. 1 (2021). https://doi.org/10.1109/TCBB.2021.3057128

3. Anastasiadou, E., Jacob, L.S., Slack, F.J.: Non-coding RNA networks in cancer. Nat. Rev. Canc. 18(1), 5–18 (2018)

4. Arnedo, J., Romero-Zaliz, R., Zwir, I., Del Val, C.: A multiobjective method for robust identification of bacterial small non-coding RNAs. Bioinformatics 30(20), 2875–2882 (2014)

5. Barman, R.K., Mukhopadhyay, A., Das, S.: An improved method for identification of small non-coding RNAs in bacteria using support vector machine. Sci. Rep. 7(1), 1–8 (2017)

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. NTpred: a robust and precise machine learning framework for in silico identification of Tyrosine nitration sites in protein sequences;Briefings in Functional Genomics;2023-05-29

2. NTpred: A robust and precise machine learning framework for in-silico identification of Tyrosine nitration sites in protein sequences;2023-02-21

3. BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria;Briefings in Bioinformatics;2022-06-27